Publications

Journal Articles

1. T.-S. T. Chan and A. Gibberd, “Feasible model-based principal component analysis: Joint estimation of rank and error covariance matrix,” submitted.

2. L. Mosley, T.-S. T. Chan and A. Gibberd, “The sparse dynamic factor model: A regularised quasi-maximum likelihood approach,” Stat. Comput., vol. 34, no. 68, Jan. 2024.

3. A. Mahdi, P. Błaszczyk, P. Dłotko, D. Salvi, T.-S. Chan, J. Harvey, D. Gurnari, Y. Wu, A. Farhat, N. Hellmer, A. Zarebski, B. Hogan, and L. Tarassenko, “OxCOVID19 Database, a multimodal data repository for better understanding the global impact of COVID-19,” Sci. Rep., vol. 11, no. 9237, Apr. 2021.

4. Z.-C. Fan, T.-S. T. Chan, Y.-H. Yang, and J.-S. R. Jang, “Backpropagation with N-D vector-valued neurons using arbitrary bilinear products,” IEEE Trans. Neural Netw. Learn. Syst., vol. 31, no. 7, pp. 2638–2652, Jul. 2020.

5. T.-S. T. Chan and Y.-H. Yang, “Informed group-sparse representation for singing voice separation,” IEEE Signal Process. Lett., vol. 24, no. 2, pp. 156–160, Feb. 2017.

6. T.-S. T. Chan and Y.-H. Yang, “Polar n-complex and n-bicomplex singular value decomposition and principal component pursuit,” IEEE Trans. Signal Process., vol. 64, no. 24, pp. 6533–6544, Dec. 2016.

7. T.-S. T. Chan and Y.-H. Yang, “Complex and quaternionic principal component pursuit and its application to audio separation,” IEEE Signal Process. Lett., vol. 23, no. 2, pp. 287–291, Feb. 2016.

8. A. Kumar and T.-S. T. Chan, “Robust ear identification using sparse representation of local texture descriptors,” Pattern Recognition, vol. 46, no. 1, pp. 73–85, Jan. 2013.

9. T.-S. Chan and A. Kumar, “Reliable ear identification using 2-D quadrature filters,” Pattern Recognition Lett., vol. 33, no. 14, pp. 1870–1881, Oct. 2012.

10. B. Mitchinson, T.-S. Chan, J. Chambers, M. Pearson, M. Humphries, C. Fox, K. Gurney, and T. J. Prescott, “BRAHMS: Novel middleware for integrated systems computation,” Advanced Eng. Informatics, vol. 24, no. 1, pp. 49–61, Jan. 2010.

Conference Proceedings

1. T.-S. T. Chan and A. Gibberd, “Identifying metering hierarchies with distance correlation and dominance constraints,” in Proc. IEEE Int. Conf. Mach. Learn. Appl., 2022, pp. 1551–1558.

2. Z.-C. Fan, T.-S. Chan, Y.-H. Yang, and J.-S. R. Jang, “Deep cyclic group networks,” in Proc. Int. Joint Conf. Neural Netw., 2019, pp. 1–8.

3. C.-A. Yu, C.-L. Tai, T.-S. Chan, and Y.-H. Yang, “Modeling multi-way relations with hypergraph embedding,” in Proc. Int. Conf. Inform. Knowledge Manage., 2018, pp. 1707–1710.

4. C.-A. Yu, T.-S. Chan, and Y.-H. Yang, “Low-rank matrix completion over finite Abelian group algebras for context-aware recommendation,” in Proc. Int. Conf. Inform. Knowledge Manage., 2017, pp. 2415–2418.

5. Z.-C. Fan, T.-S. T. Chan, Y.-H. Yang, and J.-S. R. Jang, “Music signal processing using vector product neural networks,” in Proc. IJCNN Workshop Deep Learn. Music, 2017, pp. 26–30.

6. T.-S. Chan, T.-C. Yeh, Z.-C. Fan, H.-W. Chen, L. Su, Y.-H. Yang, and R. Jang, “Vocal activity informed singing voice separation with the iKala dataset,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., 2015, pp. 718–722.

7. A. Kumar and T.-S. Chan, “Iris recognition using quaternionic sparse orientation code (QSOC),” in Proc. CVPR Workshop Biometrics, 2012, pp. 59–64.

8. A. Kumar, T.-S. Chan, and C.-W. Tan, “Human identification from at-a-distance face images using sparse representation of local iris features,” in Proc. Int. Conf. Biometrics, 2012, pp. 303–309.

9. T.-S. T. Chan and G. A. Wiggins, “More evidence for a computational memetics approach to music information and new interpretations of an aesthetic fitness measure,” in Proc. ECAI Workshop Comput. Creativity, 2006, pp. 13–17.

10. T.-S. T. Chan and G. A. Wiggins, “A computational memetics approach to music information and aesthetic fitness,” in Proc. IJCAI Workshop Comput. Creativity, 2005, pp. 105–108.

Presentations

1. T.-S. Chan, X. Tian, K. Zheng, and A. Gibberd*, “Exploring dynamic factors of fMRI activity in the presence of sparse loadings,” presented at Int. Conf. ERCIM WG Comput. Methodol. Stat., Berlin, Germany, 2023.

2. T.-S. T. Chan, “Guided source separation for machine listening and brain-computer music interfacing,” presented at Taiwanese Music Audio Comput. Workshop, Taipei, Taiwan, 2016.

3. B. Mitchinson*, T. Chan, J. Chambers, M. Humphries, K. Gurney, and T. Prescott, “BRAHMS: Novel middleware for integrated systems computation,” presented at INCF Congr. Neuroinformatics, Stockholm, Sweden, 2008.

Theses

1. T.-S. T. Chan, “A cognitive information theory of music: A computational memetics approach,” Ph.D. dissertation, Univ. London, London, UK, 2008. (errata)